Cost-Effectiveness of Lecanemab for Individuals With Early-Stage Alzheimer Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: ε4 status. METHODS: ε4 noncarriers or heterozygous patients or not) were compared. A hybrid decision tree-Markov cohort model was constructed with 5 states: (1) MCI (Clinical Dementia Rating-Sum of Boxes [CDR-SB] 0-4.5); (2) mild dementia (CDR-SB 4.6-9.5); (3) moderate dementia (CDR-SB 9.6-16); (4) severe dementia (CDR-SB >16); and (5) death. Effectiveness was measured by quality-adjusted life years and costs from third-party and societal perspectives were estimated in 2022 US dollars over a lifetime horizon. RESULTS: ε4 genotype was cost-effective vs SoC alone, regardless of the test used to diagnose patients with early-stage AD. However, CSF assay followed by targeted treatment would become cost-effective if lecanemab is priced below $5,100 per year. These results were robust to the accuracy of diagnostic testing and rates of lecanemab discontinuation and adverse events. DISCUSSION: ε4 genotype is cost-effective vs SoC alone for patients with MCI or mild dementia due to AD. Lecanemab would be cost-effective in some settings if priced below $5,100 per year.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".